Search Results (56)

Urban ventilation corridors play a critical role in improving wind environments, mitigating the urban heat island (UHI) effect, and enhancing urban climate resilience. Traditional Computational Fluid Dynamics (CFD) methods offer high accuracy in simulating wind fields but are computationally intensive and inefficient for large-scale, multi-scenario urban planning tasks. To address this limitation, this study proposes a morphology-driven, machine learning-based framework for ventilation corridor identification. The method integrates Lattice Boltzmann Method (LBM) simulations, neighborhood-based feature normalization, and a random forest regression model to establish a predictive relationship between morphological indices and wind speed distributions under prevailing wind conditions. Input features include raw and log-transformed LBM values, neighborhood-normalized indicators within multiple radii (100–2000 m), and porosity statistics. The model is trained and validated using CFD-simulated wind speeds, with the dataset randomly divided into training (80%), validation (10%), and testing (10%) subsets. The results show that the proposed method can accurately predict spatial wind speed patterns and identify both primary and secondary ventilation corridors. Primary corridors are closely aligned with large rivers and lakes, while secondary corridors are shaped by arterial roads and localized open spaces. Compared with conventional approaches such as FAI classification, Least Cost Path (LCP), and circuit theory models, the proposed framework offers higher spatial resolution and better alignment with the CFD results while significantly reducing computational cost. This study demonstrates the feasibility of using morphological and data-driven approaches to support efficient and scalable urban ventilation analysis, providing valuable guidance for climate-responsive urban design. Full article

Against the background of global climate change receiving widespread attention, local microclimate environments have become a key focus of climate change research, which is of great significance for improving the quality of urban living environments. This study explored the quantitative coupling relationship between the built environment and the thermal comfort of complex streets. Outward blocks in Beijing were used as an example. By applying deep learning to street view images of an arterial road, we built three levels of road environmental elements for a quantitative analysis, simulated the block thermal comfort, numerically extracted the built environment factor, and derived a regression equation of the thermal comfort. The research results show that the UTCI value range of the Chaowai Block is between 28.15 °C and 47.11 °C, corresponding to human thermal sensations from slightly warm to very hot. The green rate, expressways, road width, spacious surroundings, sky, traffic, and ancillary facilities significantly affected the thermal comfort. Through the regression equation results, it can be found that the thermal comfort of different levels of roads is affected by multiple street built environment factors, and these influencing factors show differences in various levels of roads. Based on the results of the regression equation, corresponding optimization strategies were proposed to improve the thermal environment of urban streets and enhance the thermal comfort of pedestrians. Full article

A representative urban driving cycle was developed for Quito, Ecuador, using the K-Means clustering method. From 64 samples and 188,713 geospatial and speed data points, a 2870 s driving cycle was constructed to capture real-world traffic characteristics. Key parameters include an average speed of 22.68 km/h, acceleration and deceleration rates of 0.55 m/s² and −0.57 m/s², and a dwell time of 9.66%. Due to Quito’s linear urban development, where mobility is limited to north–south/south–north corridors, the driving cycle reflects frequent accelerations and decelerations along congested arterial roads. A comparative analysis with international driving cycles revealed that Quito’s traffic follows a unique pattern shaped by its geographic constraints. The HK cycle in China showed the greatest similarities, although differences in instantaneous speeds highlight the need for localized models. While this study primarily focuses on methodological robustness, the developed driving cycle provides a foundational dataset for future research on traffic flow optimization, emissions estimation, and sustainable urban mobility strategies. These insights contribute to data-driven decision-making for improving transportation efficiency and environmental impact assessment in cities with similar urban structures. Full article

Vehicle trajectory data contain extensive spatiotemporal information and are of great significance for analyzing the operational patterns of urban traffic networks, optimizing traffic signal control and achieving refined traffic management. However, due to the low penetration rate of probe vehicles and the limited coverage of fixed sensors, it remains challenging to obtain comprehensive full-sample vehicle trajectory data. To address this issue, this paper proposes a multi-source data fusion-based vehicle trajectory reconstruction method, which comprises vehicle trajectory state estimation and a self-optimization algorithm. First, the trajectory states of undetected vehicles are categorized into four types based on the trajectory states of adjacent probe vehicles. Four corresponding trajectory estimation models are then established using an extended Intelligent Driver Model to reconstruct the initial trajectories of undetected vehicles. Second, a particle filter-based trajectory self-optimization algorithm is proposed, integrating upstream and downstream fixed sensor data to iteratively correct and optimize the initial trajectories by minimizing errors, thereby enhancing trajectory accuracy and smoothness. Case studies demonstrate that the proposed method achieves outstanding performance under low PV penetration rates and in complex traffic environments. Compared to baseline methods, MAE, MAPE, and RMSE are reduced by 14.31%, 22.87%, and 13.36%, respectively. Furthermore, the reconstruction errors of the proposed method gradually decrease as traffic density and PV penetration rates increase. Notably, PV penetration has a more significant impact on model accuracy. These findings confirm the robustness and effectiveness of the proposed method in complex traffic scenarios, providing critical technical support for refined urban traffic management and optimized decision-making. Full article

This study examines how urban morphology, road configurations, and meteorological factors shape fine particulate matter (PM_2.5) dispersion in high-density urban environments, addressing a gap in block-level air quality analysis. While previous research has focused on individual street canyons, this study highlights the broader influence of building arrangement and height. Integrating computational fluid dynamics (CFD) simulations with interpretable machine learning (ML) models quantifies PM_2.5 concentrations across various urban configurations. CFD simulations were conducted on different road layouts, block height configurations, and aspect ratio (AR) levels. The resulting dataset trained five ML models with Extreme Gradient Boosting (XGBoost), achieving the highest accuracy (91–95%). Findings show that road-specific mitigation strategies must be tailored. In loop-road networks, centrally elevated buildings enhance ventilation, while in grid-road networks, taller perimeter buildings shield inner blocks from arterial emissions. Additionally, this study identifies a threshold effect of AR, where values exceeding 2.5 improve PM_2.5 dispersion under high wind velocity. This underscores the need for wind-sensitive designs, including optimized wind corridors and building alignments, particularly in high-density areas. The integration of ML with CFD enhances predictive accuracy, supporting data-driven urban planning strategies to optimize road layouts, zoning regulations, and aerodynamic interventions for improved air quality. Full article

The use of traffic simulation to analyze traffic safety and performance has become common in transportation engineering. Microsimulation methods are increasingly used to analyze driving performance for different road geometries and environmental elements. Drivers’ perception has an important impact on driving performance factors contributing to traffic safety on transportation facilities (highways, arterials, intersections, etc.). Impaired vision leads to failure in drivers’ perception and making right decisions. Various studies investigated the impact of environmental elements (fog, rain, snow, etc.) on driving performance. However, there is limited research examining the potentially detrimental effects on driving capabilities due to differing exposure to natural light brightness, in particular sun exposure. Autonomous vehicles (AVs) showed a significant impact enhancing traffic capacity and improving safety margins in car-following models. AVs may also enhance and/or complement human driving under deteriorated driving conditions such as sun glare. This study uses a calibrated traffic simulation and surrogate safety assessment model to improve traffic operations and safety performance under impaired visibility using different types of autonomous vehicles. A combination of visibility reduction, traffic flow characteristics, and autonomy levels of AVs was simulated and assessed in terms of the number of conflicts, severity level, and traffic operations. The simulation analysis results used to reveal the contribution of conflicts to the risk of crashes varied based on the influence of autonomy level on safe driving during sun glare exposure. The outcome of this study indicates the benefits of using different levels of AVs as a solution to driving under vision impairment situations that researchers, traffic engineers, and policy makers can use to enhance traffic operation and road safety in urban areas. Full article

Reducing vehicle lane widths has been proposed as an effective strategy to decrease vehicle speeds and enhance road safety. However, the safety benefits of narrower travel lanes remain a topic of debate due to mixed findings in the literature. This study examines the relationship between lane width, vehicle speed, and crash occurrence to comprehensively understand their impact on road safety and transportation planning. Using data from 320 urban arterial sections in Utah, the analysis reveals that narrower lane widths are associated with reduced vehicle speeds. For every additional foot of lane width, 85th and 95th percentile speeds increase by 1.012 mph and 1.088 mph, respectively. Furthermore, injury crash modeling indicates that a one-foot increase in lane width is associated with a 38.3% increase in the odds of an injury crash on a roadway section. These findings contribute to the growing evidence supporting the implementation of narrower lane widths as a strategy to improve road safety, foster multimodal infrastructure, and promote sustainable urban transportation systems. We recommend that UDOT adopt a minimum lane width of 10 or 11 feet for arterials in highly urbanized areas, such as downtowns and major activity centers. Full article

Arterial green wave control can effectively reduce the delay time and number of stops of the coordinated traffic flows. However, existing arterial green wave control methods mostly focus on motor vehicles and provide them with green wave bands, neglecting the electric bicycles that are widespread on the roads. In fact, electric bicycles have become an important tool for short-to-medium trips among urban residents because they are convenient, low-cost, and eco-friendly. To tackle this, an arterial multi-path green wave control model that considers both motor vehicles(cars and buses) and electric bicycles is presented in this paper. The presented model is formulated as a mixed integer linear programming problem. The optimization objective of the model is to maximize the sum of the green wave bandwidths for all coordinated paths of each traffic mode on all road segments. The key constraints of the presented model can be addressed by analyzing the relationships among the green wave bandwidth, coordinated path, common cycle time, offset, phase sequence, etc., to utilize the time–space diagram. The results of the numerical example show that compared with the traditional model for through motor vehicles (cars and buses), the total green wave bandwidths of cars, buses, and electric bicycles generated by the presented model at the entire arterial level has been increased by 36.8%, 47.9%, and 19.3%, respectively. Full article

Exploring the coupled coordination and interaction between urban transport superiority degree (UTSD) and urban land use efficiency (ULUE) is the key to promoting efficient land use in cities and coordinated development. This paper adopts the improved UTSD model, super-efficiency slack-based measure–undesirable output model, coupling coordination degree model (CCDM), panel Granger causality test, random forest model, and the mixed geographically and temporally weighted regression model to reveal the spatial and temporal evolution and coupling characteristics of UTSD and ULUE in Gansu from 2005 to 2020 and to validate and explore the interaction mechanism between UTSD and ULUE. The results show that (1), from 2005 to 2020, the average UTSD in Gansu increased from 0.56 to 1.01 and the Belt and Road Initiative accelerated the construction of the transportation network in Gansu. The average ULUE increased from 0.52 to 0.62; the spatial distribution of ULUE was high in the west and north and low in the east and south. (2) From 2005 to 2020, the average CCDM of UTSD and ULUE in Gansu increased from slightly unbalanced (0.37) to slightly balanced (0.52). A spatially high UTSD and high ULUE agglomeration area can be found along the transportation arteries. (3) The UTSD and ULUE were mutually causal, with the degree of transportation arterial influence degree being the strongest driver of ULUE among the components of UTSD (30.41% contribution) and tax revenue being the strongest driver of UTSD among the components of ULUE (15.10% contribution). Overall, the connotation of ULUE puts forward the demand for improving the transportation infrastructure and, at the same time, provides the guarantee for UTSD upgrading, which in turn affects the ULUE. In the future, the Xinan region of Gansu should prioritize planning and construction of a transportation network. The results of this study can provide a scientific basis for the construction of transportation networks and the efficient use of urban land in Gansu and other regions. Full article

This paper used pavement condition data collected by the Federal Highway Administration (FHWA) between 2001 and 2006 aggregated by U.S. states to identify macroscopic factors affecting pavement roughness in time and space. To account for prior pavement conditions and preservation expenditure over time, time autocorrelation parameters were introduced in a spatial modeling scheme that accounted for spatial autocorrelation and heterogeneity. The proposed framework accommodates data aggregation in network-level pavement deterioration models. Because pavement roughness across different roadway classes is anticipated to be affected by different explanatory parameters, separate time–space models are estimated for nine roadway classes (rural interstate roads, rural collectors, urban minor arterials, urban principal arterials, and other freeways). The best model specifications revealed that different time–space models were appropriate for pavement performance modeling across the different roadway classes. Factors that were found to affect state-level pavement roughness in time and space included preservation expenditure, predominant soil type, and predominant climatic conditions. The results have the potential to assist governmental agencies in planning effectively for pavement preservation programs at a macroscopic level. Full article

Stormwater runoff from large roads is a major source of pollutants to receiving waters, and reduction of these pollutants is important for sustainable water resources and transportation networks. Porous pavements have been shown to substantially reduce many of these pollutants, but studies are lacking on arterial roads. We sampled typical stormwater pollutants in runoff from sections of an arterial road 9–16 years after installation of three pavement types: control with conventional asphalt, porous asphalt overly, and full-depth porous asphalt. Both types of porous pavements substantially reduced most of the stormwater pollutants measured. Total suspended solids, turbidity, total lead, total copper, and 6PPD-quinone were all reduced by >75%. Total nitrogen, ammonia, total phosphorus, biochemical oxygen demand, total and dissolved copper, total mercury, total zinc, total polycyclic aromatic hydrocarbons, and di-2-ethylhexyl phthalate were all reduced by >50%. Reductions were lower or absent for nitrate, orthophosphate, E. coli, dissolved lead, and dissolved zinc. Most reductions were statistically significant. Many pollutants exceeded applicable water quality standards in the control samples but met them with both types of porous pavement. This study demonstrates that porous overlays and full-depth porous asphalt can provide substantial reductions of several priority stormwater pollutants on arterial roads for many years after installation. Porous pavements have the potential to substantially enhance water quality of urban waterways and provide ecological benefits on urban thoroughfares. Full article

Road silt loading (sL) directly affects the fugitive road dust (FRD) emission factor, which is an important parameter in the study of FRD emissions. In this study, an improved collection method combined with the AP−42 method was newly developed to estimate the sL of asphalt roads in Jinan, China. The characteristics of sL in Jinan followed the order National highway (NH) > Branch road (BR) > Provincial highway (PH) > Country highway (CH) > Minor arterial (MiA) > Major arterial (MaA) > Urban expressway (UE) with 3.9 ± 2.5, 3.9 ± 1.9, 3.8 ± 2.8, 3.8 ± 0.9, 2.1 ± 1.4, 1.7 ± 1.2, and 1.4 ± 1.2 g/m², respectively. The size orders of PM_2.5 and PM₁₀ emission factors are consistent with total suspended particulate (TSP). The characteristics of the TSP emission factor of FRD followed the order NH > PH > CH > Expressway (EW) > MiA > BR > MaA > UE with 27.3, 23.4, 19.4, 13.7, 7.7, 7.4, 6.2, and 3.0 g/VKT (vehicle kilometers traveled), respectively. The annual emissions of TSP, PM₁₀, and PM_2.5 from FRD in Jinan in 2020 were about 985.2, 209.8, and 57.8 kt, respectively. Laiwu, Jiyang, and Licheng districts show the top three TSP emissions of FRD; the sum of their emissions accounts for 44.7% of the TSP emissions from FRD in Jinan. TSP emissions from municipal roads and administrative roads accounted for about 29.2% and 70.8% of the total emissions in Jinan, respectively, of which emissions from MiA accounted for the largest proportion of TSP emissions from municipal roads, contributing about 37.9%, while TSP emissions from NH made the largest contribution to TSP emissions from administrative roads, with a contribution of about 35.8%. Based on Monte Carlo simulation results using Crystal Ball, the uncertainty range of the emission inventory of FRD in Jinan ranged from −79.9 to 151.8%. In 2020, about 985,200 tons of road particulate matter in Jinan City entered the atmosphere, having an adverse effect on air quality and human health. Full article

Globally, road traffic collisions cause over a million deaths annually, with pedestrians accounting for 23%. In developing countries, most pedestrian deaths occur on urban arterial roads, particularly at midblock crossings. This study analyzes the factors influencing driver yielding behavior at midblock crosswalks on urban arterial roads in Thailand. This study analyzed the factors influencing driver yielding behavior at the midblock crosswalk before and after the upgrade from a zebra crossing (C1) to a smart pedestrian crossing (C2), which is a smart traffic signal detecting and controlling pedestrians and vehicles entering the crosswalk. Video-based observations were used to assess driver yielding behavior, with multinomial logistic regression applied to develop driver yielding behavior models. The results revealed that the chances of a driver yielding at C2 were higher than at C1, and the yielding rate increased by 74%. The models indicate that the number and width of traffic lanes, width and length of crosswalks, vulnerable group, number of pedestrians, pedestrian crossing time, number of vehicles, vehicle speed, headway, post-encroachment time between a vehicle and pedestrian, and roadside parking are the significant factors influencing yielding behavior. These findings propose measures to set proper crosswalk improvements (e.g., curb extensions), speed reduction measures, enforcement (e.g., parking restrictions), public awareness campaigns, and education initiatives. Full article

Using the Urban Network Analysis Tool, the centrality of a road network (closeness centrality, betweenness centrality, and straightness centrality) was calculated, and the POI data of the commercial facilities were reclassified. KDE estimation was used to estimate the centrality of the traffic network, and the correlation coefficient was calculated to explore the spatial relationship between road network centrality and the types of commercial facilities (catering facilities, shopping facilities, residential life facilities, and financial and insurance facilities). The results indicate the following: (1) Closeness centrality displays a discernible “Core–Periphery” pattern, and the high-value areas of betweenness centrality are mainly concentrated around the main arterial roads of the city. In contrast, straightness centrality unveils a polycentric structure. (2) The spatial distribution of commercial facilities demonstrates a notable correlation with the centrality of the road network. From the perspective of centrality, the distribution of residential life facilities is most strongly influenced by road network centrality, followed by financial and insurance facilities and then catering facilities, with the distribution of shopping facilities being the least affected. (3) The centrality of the road network plays a crucial role in shaping the arrangement of commercial facilities. Closeness centrality significantly influences the distribution of residential life facilities, catering facilities, and shopping facilities. Betweenness centrality has a noteworthy impact on the selection of locations for financial and insurance facilities, as well as residential life facilities. Furthermore, areas characterized by better straightness centrality are preferred for the distribution of residential life facilities, financial and insurance facilities, and catering facilities. (4) The centrality of the road network has a greater influence on the arrangement of various commercial facilities than the population distribution. Full article

Historic urban areas are the foundations of urban development. Due to rapid urbanization, the sustainable development of historic urban areas has become challenging for many cities. Elements of tourism and tourism service facilities play an important role in the sustainable development of historic areas. This study analyzed policies related to tourism in Panguifang and Meixian districts in Meizhou, Guangdong, China. Kernel density estimation was used to study the clustering characteristics of tourism elements through point of interest (POI) data, while space syntax was used to study the accessibility of roads. In addition, the Pearson correlation coefficient and regression were used to analyze the correlation between the elements and accessibility. The results show the following: (1) the overall number of tourism elements was high on the western side of the districts and low on the eastern one, and the elements were predominantly distributed along the main transportation arteries; (2) according to the integration degree and depth value, the western side was easier to access than the eastern one; and (3) the depth value of the area negatively correlated with kernel density, while the degree of integration positively correlated with it. Based on the results, the study put forward measures for optimizing the elements of tourism in Meizhou’s historic urban area to improve cultural tourism and emphasize the importance of the elements. Full article